ReviewFrontiers in bioinformatics2023
Geometric deep learning as a potential tool for antimicrobial peptide prediction.
Review in Frontiers in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 35 citations in OpenAlex.
- INBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computer-Aided Design ofACS medicinal chemistry letters · 2026Article
- Review
- SGAC: a graph neural network framework for imbalanced and structure-aware AMP classification.Briefings in bioinformatics · 2026Article
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.Frontiers in microbiology · 2026Article
- Recent advances in computational antimicrobial peptide discovery through big data, modeling, and artificial intelligence and their interplay in ushering the next golden era of drug development.Frontiers in bioinformatics · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Antimicrobial peptides: structure, functions and translational applications.Nature reviews. Microbiology · 2025Review
- Multimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features.Briefings in bioinformatics · 2025Article
- Article
- AI Methods for Antimicrobial Peptides: Progress and Challenges.Microbial biotechnology · 2025Review
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies.Acta pharmaceutica Sinica. B · 2024Article
- Development of a Selective and Stable Antimicrobial Peptide.ACS infectious diseases · 2024Article
- Structure-aware machine learning strategies for antimicrobial peptide discovery.Scientific reports · 2024Article
- Article
- Capping motifs in antimicrobial peptides and their relevance for improved biological activities.Frontiers in chemistry · 2024Review
- Computationally Designed AMPs with Antibacterial and Antibiofilm Activity against MDRAntibiotics (Basel, Switzerland) · 2023Article
- Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges.Annals of medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.